Machine Learning (ML) Platforms Can Contradict Dairy Scientists and Feed Firm Websites Regarding Dairy Cattle Performance from Feeding Seaweed Supplements

O’Keefe, Siobhan, Rick Welsh, Mercy Oppong, Ryan Fitzgerald, David Conner, Michelle Tynan, Nichole Price and Charlotte Quigle

Choices: The Magazine of Food, Farm, and Resource Issues, 2024, vol. 39, issue 3

Abstract: Artificial intelligence through machine learning applications (hereafter ML) is emerging as a tool in evaluating, comparing, and going beyond human capabilities and knowledge. Despite the potential benefits of ML as a resource for answering scientific questions, such as those included in our analysis, some characteristics of ML-generated responses limit the interpretations of these results—such as ML “hallucinations”—of which researchers should be aware (McIntosh et al., 2023). Nonetheless, ML is quickly becoming a source for authoritative and trusted information on many topics (Knight, 2024; McIntosh et al., 2024), as university-based and other more rigorous research may be behind paywalls or otherwise difficult to access and as pay-to-play journals proliferate. Therefore, it is useful to conduct analyses comparing ML-generated information to traditionally trusted information sources, such as scientists’ observations, and to self-interested commercial information available to the public.

Keywords: Climate Change; Environmental Economics and Policy; Research and Development/Tech Change/Emerging Technologies (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:ags:aaeach:344826

DOI: 10.22004/ag.econ.344826

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